BackgroundAs a high prevalence disorder， schizophrenia has caused significant burden to family and society due to the impairment of occupational and social function. Currently， the dominant vocational training model in China follows a paternalistic， clinician-led decision-making approach. Although it improves patients' social function to some extent， it undermines their autonomy and treatment adherence. Therefore， it is urgently necessary to explore a new intervention method to enhance treatment compliance and social function in patients. ObjectiveTo explore the impact of shared decision-making oriented vocational training on social function in hospitalized schizophrenia patients， so as to provide references for rehabilitation interventions. MethodsA total of 68 patients diagnosed with schizophrenia according to the International Classification of Diseases， tenth edition （ICD-10） criteria were consecutively enrolled from January to June 2024 at The Third People's Hospital of Wenjiang Distric， Chengdu. Participants were randomly allocated into the research group （n=34） and the control group （n=34） using a random number table method. Both groups received routine rehabilitation training， while the research group received shared decision-making oriented vocational training for 12 weeks， 2 times a week for 2 hours each time. Before and at the 4^th and 12^th week of intervention， two groups were evaluated by General Self-Efficacy Scale （GSES）， Stigma Scale for Mental Illness （SSMI）， Scale of Social function of Psychosis Inpatients （SSFPI） and Inpatient Psychiatric Rehabilitation Outcome Scale （IPROS）. ResultsA total of 63 participants completed the study， with 30 cases in the research group and 33 cases in the control group. Repeated measures ANOVA revealed statistically significant time effects and interaction effects in both groups for GSES， SSMI， SSFPI and IPROS scores （F=20.451， 16.022； 26.193， 12.944； 23.957， 5.023； 11.776， 3.985， P<0.05 or 0.01）， while no significant group effects were observed （F=0.188， 0.742， 1.878， 0.474， P>0.05）. At the 12^th week of intervention， there were statistically significant differences in GSES， SSMI， SSFPI and IPROS scores between the two groups. ConclusionShared decision-making oriented vocational training may help to improve social function in patients with schizophrenia. ［Funded by 2023 Chengdu Medical Research Project （number， 2023468）］

Objective To investigate the independent risk factors affecting the prognosis of chronic active antibody-mediated rejection (caAMR) after kidney transplantation. Methods A retrospective analysis was conducted on 61 patients who underwent renal biopsy and were diagnosed with caAMR. The patients were divided into caAMR group (n=41) and caAMR+TCMR group (n=20) based on the presence or absence of concurrent acute T cell-mediated rejection (TCMR). The patients were followed up for 3 years. The value of 24-hour urinary protein and estimated glomerular filtration rate (eGFR) at the time of biopsy in predicting graft loss was assessed using receiver operating characteristic (ROC) curves. The independent risk factors affecting caAMR prognosis were analyzed using the LASSO-Cox regression model. The correlation between grouping, outcomes, and Banff scores was compared using Spearman rank correlation matrix analysis. Kaplan-Meier analysis was used to evaluate the renal allograft survival rates of each subgroup. Results The 3-year renal allograft survival rates for the caAMR group and the caAMR+TCMR group were 83% and 79%, respectively. The area under the ROC curve (AUC) for predicting 3-year renal allograft loss was 0.83 ［95% confidence interval (CI) 0.70-0.97］ for eGFR and 0.78 (95% CI 0.61-0.96) for 24-hour urinary protein at the time of biopsy. LASSO-Cox regression analysis and Kaplan-Meier analysis showed that eGFR≤25.23 mL/(min·1.73 m²) and the presence of donor-specific antibody (DSA) against human leukocyte antigen (HLA) class I might be independent risk factors affecting renal allograft prognosis, with hazard ratios of 7.67 (95% CI 2.18-27.02) and 5.13 (95% CI 1.33-19.80), respectively. A strong correlation was found between the Banff chronic lesion indicators of renal interstitial fibrosis and tubular atrophy (P<0.05). Conclusions The presence of HLA class I DSA and eGFR≤25.23 mL/(min·1.73 m²) at the time of biopsy may be independent risk factors affecting the prognosis of caAMR.

Objective To explore the role of neogambogic acid in the characteristics of colorectal cancer stem cells (CRC-CSCs) through the Wnt/β-catenin signaling pathway. Methods The colorectal cells SW480 and HCT166 were divided into control group and neogambogic acid groups (1.5, 3, 6, and 12 μmol/L). The viability of CRC-CSCs was determined by MTT method, and spheroid and clone formation assays were used to assess the capacity of spheroid formation and self-renewal ability of the cells. The effects of neogambogic acid on the apoptosis and cell cycle of CRC-CSCs were evaluated by flow cytometry assays. Real-time quantitative PCR was used to detect the mRNA expression levels of relative markers (CD133, CD44, ALDH1, Oct4, and Nanog) of CRC-CSCs, and the protein expression levels of the self-renewal marker (PCNA), apoptosis markers (cleaved caspase-3 and cleaved caspase-9), and Wnt/β-catenin signaling pathway markers (p-GSK3β, GSK3β, β-catenin, and Wnt) were analyzed using Western blot. Results Compared with the control group, after neogambogic acid treatment, the viability of SW480 and HCT116 cells decreased (P<0.05), the spheroid forming ability and the clone numbers of CRC-CSCs decreased (P<0.001, P<0.01) but the cell apoptosis rate increased (P<0.01), and cell cycle was arrested in G₀/G₁ phase. Moreover, neogambogic acid downregulated the mRNA and protein expression of relative markers of CRC-CSCs (CD133, CD44, ALDH1, Oct4, and Nanog), PCNA, p-GSK3β, β-catenin, and Wnt (P<0.05) and upregulated the expression of cleaved caspase-3, cleaved caspase-9, and GSK3β (P<0.01). Conclusion Neogambogic can inhibit the stem cell properties of colorectal cells via inhibition of Wnt/β-catenin signaling pathway. As a result, neogambogic acid may be an attractive agent against colorectal cancer.

Objective To construct large medical model named by "Huaxi HongYi"and explore its application effectiveness in assisting medical record generation. Methods By the way of a full-chain medical large model construction paradigm of "data annotation - model training - scenario incubation", through strategies such as multimodal data fusion, domain adaptation training, and localization of hardware adaptation, "Huaxi HongYi" with 72 billion parameters was constructed. Combined with technologies such as speech recognition, knowledge graphs, and reinforcement learning, an application system for assisting in the generation of medical records was developed. Results Taking the assisted generation of discharge records as an example, in the pilot department, after using the application system, the average completion times of writing a medical records shortened (21 min vs. 5 min) with efficiency increased by 3.2 time, the accuracy rate of the model output reached 92.4%. Conclusion It is feasible for medical institutions to build independently controllable medical large models and incubate various applications based on these models, providing a reference pathway for artificial intelligence development in similar institutions.

Objective: To understand the dynamic temporal evolution pathways of Internet addiction among university students and to identify the core driving nodes, so as to provide theoretical evidences for the precise implementation of targeted interventions. Methods: Using a convenient cluster sampling method, a total of 1 066 full time freshmen and sophomores were recruited from three universities in Guizhou, Jiangxi, and Guangdong Provinces for a follow up survey (T1:January-March 2024; T2:January-March 2025). The Revised Chen Internet Addiction Scale (CIAS-R) was employed to assess the status of Internet addiction among university students, and cross sectional as well as cross lagged panel network models were constructed to analyze Internet addiction and its multidimensional influencing factors. Results: The T1 network comprised 19 nodes and 114 non zero edges, while the T2 network comprised 19 nodes and 126 non zero edges. Cross sectional network analysis revealed the strongest association between "insufficient sleep" and "daytime fatigue"; the core nodes were "first thought upon waking for going online" and "feeling low after disconnection" (characteristics of psychological dependence) at T1, while the core nodes shifted to "impaired health" and "excitement when online" (characteristics of functional impairment and addictive psychodynamic features) at T2. Cross lagged network analysis further indicated that "reduced leisure" directly predicted "sleep compression", and a bidirectional relationship was observed between "needing more time to achieve satisfaction" and "academic decline". Conclusions Internet addiction among university students exhibits dynamic evolutionary characteristics. Stage specific targeted interventions focusing on core driving nodes are needed, integrating behavioral regulation and academic support to break the vicious cycle and enhancing the ability to cope with real life demands.

BACKGROUND:Acupoint embedding and floating needle therapy are two methods for the treatment of knee osteoarthritis,but there are few reports on the combined treatment of the two methods. OBJECTIVE:To investigate the therapeutic effect of acupoint embedding combined with floating needle therapy on different stages of knee osteoarthritis using generalized estimating equations. METHODS:A total of 436 patients with knee osteoarthritis admitted to Dongguan Hospital of Traditional Chinese Medicine from February 2019 to February 2023 were selected as the research subjects.All patients were randomly divided into a control group with floating needle therapy(n=218)and an observation group with acupoint embedding method combined with floating needle therapy(n=218).Staging was performed according to the K-L staging method.In the control group,there were 57 cases in stage Ⅰ,62 in stage Ⅱ,49 in phase Ⅲ,and 50 in stage Ⅳ,while in the observation group,there were 48 cases in stage Ⅰ,66 in stage Ⅱ,63 in phase Ⅲ,and 41 in stage Ⅳ.The levels of indexes and clinical efficacy were compared between groups before and after treatment.Generalized estimating equation model was used to analyze the influencing factors of clinical efficacy and the interaction effect of different time points,different methods and different stages on therapeutic efficacy. RESULTS AND CONCLUSION:There was no significant difference in baseline data between the observation group and the control group,as well as between the patients of different stages(P>0.05).After treatment,the cure rate of stage Ⅰ patients was the highest after treatment,and the total improvement rate in the observation group was significantly higher than that in the control group.There were significant differences in the cure rate among different stages in each group(P<0.05).After treatment,all indicators in the two groups were significantly decreased.In the control group,there were significant differences in various indicators of patients at different stages after 4 weeks of treatment(P<0.05).In the observation group,after 2 weeks of treatment,there were significant differences in various indicators of patients at different stages(P<0.05),and all the indexes in the observation were lower than those in the control group after 4 weeks of treatment(P<0.05)and the therapeutic effect in the observation group was better than that in the control group.Generalized estimating equation model analysis showed that trauma history,interleukin-6 level,treatment method,treatment time and K-L stage all significantly affected the clinical efficacy in patients.In the interaction effect analysis,after 2 weeks of treatment,there was a significant difference in the visual analogue scale score between the two groups in stages Ⅲ and Ⅳ(P<0.05).After 4 weeks of treatment,there was a significant difference in the visual analogue scale score between the two groups in stages Ⅱ,Ⅲ,and Ⅳ(P<0.05).To conclude,acupoint embedding combined with floating needle therapy is superior to floating needle therapy alone in the treatment of different stages of knee osteoarthritis.Trauma history,interleukin-6 level,treatment method,treatment time and K-L stage significantly influence the therapeutic effect.

Objective To explore enteral nutrition support and analyze its influencing factors in elderly patients with ischemic stroke. Methods A total of 328 patients with ischemic stroke in General Hospital of Western Theater Command were enrolled for nutritional screening between July 2020 and February 2024. Corresponding nutritional support plans were selected to investigate the compliance of patients with enteral nutrition support. Patients were divided into a standard group (n=140) and a non-standard group (n=97) based on whether their calorie intake met the standard. The effects of different clinical characteristics on enteral nutrition support were explored, and logistic analysis was used to analyze the influencing factors of non-standard enteral nutrition support. Results In the 328 patients with ischemic stroke, proportions of total parenteral nutrition support, total enteral nutrition support, and parenteral/enteral nutrition support were 25.30%, 27.74% and 46.95%, respectively. The proportions of vomiting or regurgitation, gastric residual volume >100 mL, mechanical ventilation and use of antibiotics >2 in the non-standard group were higher than those in the standard group (P<0.05). Logistic analysis showed that the above clinical characteristics were risk factors influencing patients with enteral nutrition support and parenteral/enteral nutrition support. Conclusion Vomiting or regurgitation , gastric residual volume, mechanical ventilation, and amount of antibiotics used are important influencing factors of enteral nutrition support in patients. Clinicians should pay attention to the above clinical characteristics.

ObjectiveTomatoes are one of the highest-yielding and most widely cultivated economic crops globally, playing a crucial role in agricultural production and providing significant economic benefits to farmers and related industries. However, early blight in tomatoes is known for its rapid infection, widespread transmission, and severe destructiveness, which significantly impacts both the yield and quality of tomatoes, leading to substantial economic losses for farmers. Therefore, accurately identifying early symptoms of tomato early blight is essential for the scientific prevention and control of this disease. Additionally, visualizing affected areas can provide precise guidance for farmers, effectively reducing economic losses. This study combines hyperspectral imaging technology with machine learning algorithms to develop a model for the early identification of symptoms of tomato early blight, facilitating early detection of the disease and visual localization of affected areas. MethodsTo address noise interference present in hyperspectral images, robust principal component analysis (RPCA) is employed for effective denoising, enhancing the accuracy of subsequent analyses. To avoid insufficient information representation caused by the subjective selection of regions of interest, the Otsu’s thresholding method is utilized to extract tomato leaves effectively from the background, with the average spectrum of the entire leaf taken as the primary object of study. Furthermore, a comprehensive spectral preprocessing workflow is established by integrating multivariate scatter correction (MSC) and standardization methods, ensuring the reliability and effectiveness of the data. Based on the processed spectral data, a discriminant model utilizing a linear kernel function support vector machine (SVM) is constructed, focusing on characteristic wavelengths to improve the model's discriminative capability. ResultsCompared to full-spectrum modeling, this approach results in an 8.33% increase in accuracy on the test set. After optimizing the parameters of the SVM model, when C=1.64, the accuracies of the training set and test set reach 91.67% and 94.44%, respectively, demonstrating a 1.19% increase in training set accuracy compared to the unoptimized model, while maintaining the same accuracy on the test set, effectively alleviating issues of underfitting. ConclusionThis study successfully establishes an early discriminant model for tomato early blight using hyperspectral imaging and achieves visualization of early symptoms. Experimental results indicate that the SVM discriminant model based on characteristic wavelengths and a linear kernel function can effectively identify early symptoms of tomato early blight. Visualization of these symptoms in terms of disease probability allows for a more intuitive detection of early diseases and timely implementation of corresponding control measures. This visual analysis not only enhances the efficiency of disease identification but also provides farmers with more straightforward and practical information, aiding them in formulating more reasonable prevention strategies. These research findings provide valuable references for the early identification and visualization of plant diseases, holding significant practical implications for monitoring, identifying, and scientifically preventing crop diseases. Future research could further explore how to apply this model to disease detection in other crops and how to integrate IoT technology to create intelligent disease monitoring systems, enhancing the scientific and efficient management of crops.

By analyzing the current application of multi-modal data in the diagnosis of gastric "inflammation-cancer" transformation， this study explored the feasibility and strategies for constructing a disease-syndrome integrated diagnosis and treatment system. Based on traditional Chinese medicine （TCM） phenomics， we proposed utilizing multi-modal data from literature research， cross-sectional studies， and cohort follow-ups， combined with artificial intelligence technology， to establish a multi-dimensional diagnostic and treatment index system. This approach aims to uncover the complex pathogenesis and transformation patterns of gastric "inflammation-cancer" progression. Additionally， by dynamically collecting TCM four-diagnostic information and modern medical diagnostic information through a long-term follow-up system， we developed three major modules including information extraction， multi-modal phenotypic modeling， and information output， to make it enable real-world clinical data-driven long-term follow-up and treatment of chronic atrophic gastritis. This system can provide technical support for clinical diagnosis， treatment evaluation， and research， while also offering insights and methods for intelligent TCM diagnosis.

Twelve compounds were isolated from the rice fermentation extracts of Penicillium expansum GY618 by silica column chromatography, Sephadex gel column chromatography, ODS column chromatography and semi preparative HPLC methods. They were determined as 11-hydroxyl-penicitrinone F (1), penicitrinone F (2), betulin (3), erythrodiol (4), ergosterol (5), ergost-5α,8α-epidioxy-6,22-dien-3β-ol (6), (5α,8α-epidioxy-(22E,24R)-ergosta-6,9(11),22-trien-3β-ol) (7), 5α,8α-epidioxy-(22E,24R)-23-methylergosta-6,22-dien-3β-ol (8), 5α,8α-epidioxy- 23,24(R)-dimethylcholesta-6,9(11),22-trien-3β-ol (9), dankasterone A (10), (17R)-4-hydroxy-17-methylincisterol (11) and ergosta-4,6,8(14),22-tetraen-3-one (12), through mass spectrometer, nuclear magnetic resonance (NMR) and comparison with the literature. Compound 1 was a new compound and compounds 2-4, 6-12 were isolated from Penicillium expansum fungus for the first time. The tyrosinase inhibitory activity experiment showed that compounds 1, 3 and 12 showed certain inhibitory activity against tyrosinase with IC₅₀ values of (75 ± 9), (69 ± 8) and (64 ± 2) μmol·L^-1, respectively. The IC₅₀ of other compounds were all greater than 100 μmol·L^-1, while IC₅₀ of the positive control kojic acid was (46 ± 4) μmol·L^-1.